Dual analysis for recommending developers to resolve bugs

Xin Xia, David Lo, Xinyu Wang, Bo Zhou

Research output: Contribution to journalArticleResearchpeer-review

41 Citations (Scopus)

Abstract

Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method that performs two kinds of analysis: bug reports based analysis (BR-Based analysis) and developer based analysis (D-Based analysis). We evaluate our solution on five large bug report datasets including GNU Compiler Collection, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 107,875 bug reports. We show that DevRec could achieve recall@5 and recall@10 scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. The results show that DevRec on average improves recall@5 and recall@10 scores of Bugzie by 57.55% and 39.39%, outperforms DREX by 165.38% and 89.36%, and outperforms NonTraining by 212.39% and 168.01%, respectively. Moreover, we evaluate the stableness of DevRec with different parameters, and the results show that the performance of DevRec is stable for a wide range of parameters.

Original languageEnglish
Pages (from-to)195-220
Number of pages26
JournalJournal of Software: Evolution and Process
Volume27
Issue number3
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Composite
  • Developer recommendation
  • Multi-label learning
  • Topic model

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